Are Natural Resources Cursed? An Investigation of the Dynamic Effects of Resource Dependence on Institutional Quality
Bibliographic record
Abstract
This paper examines whether natural resource dependence has a negative influence on various indicators of institutional quality when controlling for the potential effects of other geographic, economic and cultural initial conditions. Analysis of a panel of countries from 1996 to 2010 indicates that a high degree of resource dependence, measured as the share of mineral fuel exports in a country's total exports, is associated with worse government effectiveness, as well as with reduced levels of competition across the economy. Furthermore, estimation of long-run elasticities suggests that government effectiveness and the intensity of domestic competition decrease over time as the dependence on natural resources increases. An illustration of the Russian case shows that the negative effects accumulate in the long run, leading to a worse deterioration of government effectiveness in Russia than in Canada, a country with a comparable resource endowment but far better overall institutional quality. This result is corroborated by a significant negative correlation found between regional resource dependence and an indicator of regulatory capture in Russian regions, which indicates that the regulatory environment is more likely to be subverted in regions that are more dependent on extractive industries. Overall, the findings would be consistent with a situation in which a generally weak institutional environment would allow resource interests to wield the bidding power accruing from export revenues to subvert the content of laws and regulations, as well as their enforcement. The fact that this is associated with negative externalities for the rest of the economy, notably by undermining a level playing field across non-resource sectors, sheds light on a potential channel for the resource curse.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".